A disposition of interpolation techniques

A large collection of interpolation techniques is available for application in environmental research. To help environmental scientists in choosing an appropriate technique a disposition is made, based on 1) applicability in space, time and space-time, 2) quantification of accuracy of interpolated values, 3) incorporation of ancillary information, and 4) incorporation of process knowledge. The described methods include inverse distance weighting, nearest neighbour methods, geostatistical interpolation methods, Kalman filter methods, Bayesian Maximum Entropy methods, etc. The applicability of methods in aggregation (upscaling) and disaggregation (downscaling) is discussed. Software for interpolation is described. The application of interpolation techniques is illustrated in two case studies: temporal interpolation of indicators for ecological water quality, and spatio-temporal interpolation and aggregation of pesticide concentrations in Dutch surface waters. A valuable next step will be to construct a decision tree or decision support system, that guides the environmental scientist to easy-to-use software implementations that are appropriate to solve their interpolation problem. Validation studies are needed to assess the quality of interpolated values, and the quality of information on uncertainty provided by the interpolation method

Environmental risk assessment in the mediterranean region using artificial neural networks

Los mapas auto-organizados han demostrado ser una herramienta apropiada para la clasificación y visualización de grupos de datos complejos. Redes neuronales, como los mapas auto-organizados (SOM) o las redes difusas ARTMAP (FAM), se utilizan en este estudio para evaluar el impacto medioambiental acumulativo en diferentes medios (aguas subterráneas, aire y salud humana). Los SOMs también se utilizan para generar mapas de concentraciones de contaminantes en aguas subterráneas simulando las técnicas geostadísticas de interpolación como kriging y cokriging. Para evaluar la confiabilidad de las metodologías desarrolladas en esta tesis, se utilizan procedimientos de referencia como puntos de comparación: la metodología DRASTIC para el estudio de vulnerabilidad en aguas subterráneas y el método de interpolación espacio-temporal conocido como Bayesian Maximum Entropy (BME) para el análisis de calidad del aire. Esta tesis contribuye a demostrar las capacidades de las redes neuronales en el desarrollo de nuevas metodologías y modelos que explícitamente permiten evaluar las dimensiones temporales y espaciales de riesgos acumulativos

Towards a Soil Information System with quantified accuracy : a prototype for mapping continuous soil properties

This report describes the potential and functionality of software for spatial analysis, prediction and stochastic simulation of continuous soil properties using data from the Dutch Soil Information System (BIS). A geostatistical framework and R codes were developed. The geostatistical model of a soil property has a deterministic component representing the mean value within a soil category, and a stochastic component of standardized residuals. The standardized residuals are interpolated or simulated based on the simple kriging system. The software was tested in four case studies: exchangeable soil pH, clay content, organic matter content and Mean Spring Water table depth (MSW). It is concluded that the geostatistical framework and R codes developed in this study enable to predict values of continuous soil properties spatially, and to quantify the inaccuracy of these predictions. The inaccuracy of a spatial prediction at a certain location is quantified by the kriging variance, which can be interpreted as an indication of the uncertainty about the true value

Image processing of multiphase images obtained via X-ray microtomography: A review

Easier access to X-ray microtomography (lCT) facilities has provided much new insight from high-resolution imaging for various problems in porous media research. Pore space analysis with respect to functional properties usually requires segmentation of the intensity data into different classes. Image segmentation is a nontrivial problem that may have a profound impact on all subsequent image analyses. This review deals with two issues that are neglected in most of the recent studies on image segmentation: (i) focus on multiclass segmentation and (ii) detailed descriptions as to why a specific method may fail together with strategies for preventing the failure by applying suitable image enhancement prior to segmentation. In this way, the presented algorithms become very robust and are less prone to operator bias. Three different test images are examined: a synthetic image with ground-truth information, a synchrotron image of precision beads with three different fluids residing in the pore space, and a lCT image of a soil sample containing macropores, rocks, organic matter, and the soil matrix. Image blur is identified as the major cause for poor segmentation results. Other impairments of the raw data like noise, ring artifacts, and intensity variation can be removed with current image enhancement methods. Bayesian Markov random field segmentation, watershed segmentation, and converging active contours are well suited for multiclass segmentation, yet with different success to correct for partial volume effects and conserve small image features simultaneously.Keywords: X-ray tomography, Soil structure, Multiphase flow, Segmentation, structure analysis, Image processin

Multi-tier framework for the inferential measurement and data-driven modeling

A framework for the inferential measurement and data-driven modeling has been proposed and assessed in several real-world application domains. The architecture of the framework has been structured in multiple tiers to facilitate extensibility and the integration of new components. Each of the proposed four tiers has been assessed in an uncoupled way to verify their suitability. The first tier, dealing with exploratory data analysis, has been assessed with the characterization of the chemical space related to the biodegradation of organic chemicals. This analysis has established relationships between physicochemical variables and biodegradation rates that have been used for model development. At the preprocessing level, a novel method for feature selection based on dissimilarity measures between Self-Organizing maps (SOM) has been developed and assessed. The proposed method selected more features than others published in literature but leads to models with improved predictive power. Single and multiple data imputation techniques based on the SOM have also been used to recover missing data in a Waste Water Treatment Plant benchmark. A new dynamic method to adjust the centers and widths of in Radial basis Function networks has been proposed to predict water quality. The proposed method outperformed other neural networks. The proposed modeling components have also been assessed in the development of prediction and classification models for biodegradation rates in different media. The results obtained proved the suitability of this approach to develop data-driven models when the complex dynamics of the process prevents the formulation of mechanistic models. The use of rule generation algorithms and Bayesian dependency models has been preliminary screened to provide the framework with interpretation capabilities. Preliminary results obtained from the classification of Modes of Toxic Action (MOA) indicate that this could be a promising approach to use MOAs as proxy indicators of human health effects of chemicals.Finally, the complete framework has been applied to three different modeling scenarios. A virtual sensor system, capable of inferring product quality indices from primary process variables has been developed and assessed. The system was integrated with the control system in a real chemical plant outperforming multi-linear correlation models usually adopted by chemical manufacturers. A model to predict carcinogenicity from molecular structure for a set of aromatic compounds has been developed and tested. Results obtained after the application of the SOM-dissimilarity feature selection method yielded better results than models published in the literature. Finally, the framework has been used to facilitate a new approach for environmental modeling and risk management within geographical information systems (GIS). The SOM has been successfully used to characterize exposure scenarios and to provide estimations of missing data through geographic interpolation. The combination of SOM and Gaussian Mixture models facilitated the formulation of a new probabilistic risk assessment approach.Aquesta tesi proposa i avalua en diverses aplicacions reals, un marc general de treball per al desenvolupament de sistemes de mesurament inferencial i de modelat basats en dades. L'arquitectura d'aquest marc de treball s'organitza en diverses capes que faciliten la seva extensibilitat així com la integració de nous components. Cadascun dels quatre nivells en que s'estructura la proposta de marc de treball ha estat avaluat de forma independent per a verificar la seva funcionalitat. El primer que nivell s'ocupa de l'anàlisi exploratòria de dades ha esta avaluat a partir de la caracterització de l'espai químic corresponent a la biodegradació de certs compostos orgànics. Fruit d'aquest anàlisi s'han establert relacions entre diverses variables físico-químiques que han estat emprades posteriorment per al desenvolupament de models de biodegradació. A nivell del preprocés de les dades s'ha desenvolupat i avaluat una nova metodologia per a la selecció de variables basada en l'ús del Mapes Autoorganitzats (SOM). Tot i que el mètode proposat selecciona, en general, un major nombre de variables que altres mètodes proposats a la literatura, els models resultants mostren una millor capacitat predictiva. S'han avaluat també tot un conjunt de tècniques d'imputació de dades basades en el SOM amb un conjunt de dades estàndard corresponent als paràmetres d'operació d'una planta de tractament d'aigües residuals. Es proposa i avalua en un problema de predicció de qualitat en aigua un nou model dinàmic per a ajustar el centre i la dispersió en xarxes de funcions de base radial. El mètode proposat millora els resultats obtinguts amb altres arquitectures neuronals. Els components de modelat proposat s'han aplicat també al desenvolupament de models predictius i de classificació de les velocitats de biodegradació de compostos orgànics en diferents medis. Els resultats obtinguts demostren la viabilitat d'aquesta aproximació per a desenvolupar models basats en dades en aquells casos en els que la complexitat de dinàmica del procés impedeix formular models mecanicistes. S'ha dut a terme un estudi preliminar de l'ús de algorismes de generació de regles i de grafs de dependència bayesiana per a introduir una nova capa que faciliti la interpretació dels models. Els resultats preliminars obtinguts a partir de la classificació dels Modes d'acció Tòxica (MOA) apunten a que l'ús dels MOA com a indicadors intermediaris dels efectes dels compostos químics en la salut és una aproximació factible.Finalment, el marc de treball proposat s'ha aplicat en tres escenaris de modelat diferents. En primer lloc, s'ha desenvolupat i avaluat un sensor virtual capaç d'inferir índexs de qualitat a partir de variables primàries de procés. El sensor resultant ha estat implementat en una planta química real millorant els resultats de les correlacions multilineals emprades habitualment. S'ha desenvolupat i avaluat un model per a predir els efectes carcinògens d'un grup de compostos aromàtics a partir de la seva estructura molecular. Els resultats obtinguts desprès d'aplicar el mètode de selecció de variables basat en el SOM milloren els resultats prèviament publicats. Aquest marc de treball s'ha usat també per a proporcionar una nova aproximació al modelat ambiental i l'anàlisi de risc amb sistemes d'informació geogràfica (GIS). S'ha usat el SOM per a caracteritzar escenaris d'exposició i per a desenvolupar un nou mètode d'interpolació geogràfica. La combinació del SOM amb els models de mescla de gaussianes dona una nova formulació al problema de l'anàlisi de risc des d'un punt de vista probabilístic

Spatiotemporal Geostatistical Methods for Exposure and Epidemiological Analyses of Groundwater Nitrate and Radon

Exposure assessment and dose-response characterization are critical steps in the risk assessment of an environmental contaminant with potential human health effects. There are many established methods to conduct exposure assessments and to characterize the dose-response relationship between a contaminant of concern and a health outcome; however, many require extensive time and monetary resources that are becoming increasingly limited. Geostatistical methods are attractive approaches due to their cost-effective implementation and clear physical interpretations. Land use regression (LUR) is a type of geostatistical method that uses spatially-based explanatory variables to model outcomes using classical regression methods. Bayesian Maximum Entropy (BME) is a geostatistical framework for incorporating measurements as well as various knowledge bases in a logical and theoretically sound manner to produce estimates for variables of interest at unmonitored locations. This work advances these spatiotemporal geostatistical methods in the following three studies: 1) An exposure assessment of groundwater nitrate (NO_3^-), a biological nutrient with natural and anthropogenic sources that in excess has deleterious effects on human and ecological health; 2) An exposure assessment of groundwater radon (222Rn), a naturally occurring gas with radioactively discharged alpha particles that are known human carcinogens; and 3) An epidemiological analysis of the association between groundwater 222Rn exposure and lung and stomach cancer incidence. First, we develop a nonlinear LUR model and then integrate the model into the BME framework to produce the first space/time exposure estimates of groundwater NO_3^- concentrations across a large domain with a cross-validation r^2of 0.74. Second, an exposure model for point-level groundwater 222Rn is developed with anisotropic geological and uranium-based explanatory variables resulting in a cross-validation r^2of 0.46. Lastly, we utilize the LUR-BME exposure model for 222Rn to investigate associations with lung and stomach cancer at multiple spatial scales. It is the first epidemiological analysis of the association between groundwater 222Rn exposure and lung cancer, moreover with a significant and positive association; and the first to find a positive association between groundwater 222Rn and stomach cancer. This body of research provides advances in exposure assessment and dose-response methodology and practical real-world examples that can be used as resources for future cost-effective protection of public health.Doctor of Philosoph

Complex land cover classifications and physical properties retrieval of tropical forests using multi-source remote sensing

The work presented in this thesis mainly focuses on two subjects related to the application of remote sensing data: (1) for land cover classification combining optical sensor, texture features generated from spectral information and synthetic aperture radar (SAR) features, and (2) to develop a non-destructive approach for above ground biomass (AGB) and forest attributes estimation employing multi-source remote sensing data (i.e. optical data, SAR backscatter) combined with in-situ data. Information provided by reliable land cover map is useful for management of forest resources to support sustainable forest management, whereas the generation of the non-destructive approach to model forest biophysical properties (e.g. AGB and stem volume) is required to assess the forest resources more efficiently and cost-effective, and coupled with remote sensing data the model can be applied over large forest areas. This work considers study sites over tropical rain forest landscape in Indonesia characterized by different successional stages and complex vegetation structure including tropical peatland forests. The thesis begins with a brief introduction and the state of the art explaining recent trends on monitoring and modeling of forest resources using remote sensing data and approach. The research works on the integration of spectral information and texture features for forest cover mapping is presented subsequently, followed by development of a non-destructive approach for AGB and forest parameters predictions and modeling. Ultimately, this work evaluates the potential of mosaic SAR data for AGB modeling and the fusion of optical and SAR data for peatlands discrimination. The results show that the inclusion of geostatistics texture features improved the classification accuracy of optical Landsat ETM data. Moreover, the fusion of SAR and optical data enhanced the peatlands discrimination over tropical peat swamp forest. For forest stand parameters modeling, neural networks method resulted in lower error estimate than standard multi-linear regression technique, and the combination of non-destructive measurement (i.e. stem number) and remote sensing data improved the model accuracy. The up scaling of stem volume and biomass estimates using Kriging method and bi-temporal ETM image also provide favorable estimate results upon comparison with the land cover map.Die in dieser Dissertation präsentierten Ergebnisse konzentrieren sich hauptsächlich auf zwei Themen mit Bezug zur angewandten Fernerkundung: 1) Der Klassifizierung von Oberflächenbedeckung basierend auf der Verknüpfung von optischen Sensoren, Textureigenschaften erzeugt durch Spektraldaten und Synthetic-Aperture-Radar (SAR) features und 2) die Entwicklung eines nichtdestruktiven Verfahrens zur Bestimmung oberirdischer Biomasse (AGB) und weiterer Waldeigenschaften mittels multi-source Fernerkundungsdaten (optische Daten, SAR Rückstreuung) sowie in-situ Daten. Eine zuverlässige Karte der Landbedeckung dient der Unterstützung von nachhaltigem Waldmanagement, während eine nichtdestruktive Herangehensweise zur Modellierung von biophysikalischen Waldeigenschaften (z.B. AGB und Stammvolumen) für eine effiziente und kostengünstige Beurteilung der Waldressourcen notwendig ist. Durch die Kopplung mit Fernerkundungsdaten kann das Modell auf große Waldflächen übertragen werden. Die vorliegende Arbeit berücksichtigt Untersuchungsgebiete im tropischen Regenwald Indonesiens, welche durch verschiedene Regenerations- und Sukzessionsstadien sowie komplexe Vegetationsstrukturen, inklusive tropischer Torfwälder, gekennzeichnet sind. Am Anfang der Arbeit werden in einer kurzen Einleitung der Stand der Forschung und die neuesten Forschungstrends in der Überwachung und Modellierung von Waldressourcen mithilfe von Fernerkundungsdaten dargestellt. Anschließend werden die Forschungsergebnisse der Kombination von Spektraleigenschaften und Textureigenschaften zur Waldbedeckungskartierung erläutert. Desweiteren folgen Ergebnisse zur Entwicklung eines nichtdestruktiven Ansatzes zur Vorhersage und Modellierung von AGB und Waldeigenschaften, zur Auswertung von Mosaik- SAR Daten für die Modellierung von AGB, sowie zur Fusion optischer mit SAR Daten für die Identifizierung von Torfwäldern. Die Ergebnisse zeigen, dass die Einbeziehung von geostatistischen Textureigenschaften die Genauigkeit der Klassifikation von optischen Landsat ETM Daten gesteigert hat. Desweiteren führte die Fusion von SAR und optischen Daten zu einer Verbesserung der Unterscheidung zwischen Torfwäldern und tropischen Sumpfwäldern. Bei der Modellierung der Waldparameter führte die Neural-Network-Methode zu niedrigeren Fehlerschätzungen als die multiple Regressions. Die Kombination von nichtdestruktiven Messungen (z.B. Stammzahl) und Fernerkundungsdaten führte zu einer Steigerung der Modellgenauigkeit. Die Hochskalierung des Stammvolumens und Schätzungen der Biomasse mithilfe von Kriging und bi-temporalen ETM Daten lieferten positive Schätzergebnisse im Vergleich zur Landbedeckungskarte

Integrated geophysical investigation to detect buried structures: examples in the south-eastern part of Rome and its surroundings (Latium, central Italy)

Nell’ambito dell’engineering geology, il problema delle cavità antropiche che interessano centri abitati è particolarmente sentito, infatti, stando a modelli geologici recentemente sviluppati, esse possono migrare verso la superfice mettendo così a repentaglio la presenza di edifici, strade e l’incolumità degli stessi abitanti. Inoltre, i metodi d’investigazione diretti (quali sondaggi geognostici e indagini dirette del reticolo caveale) risultano essere piuttosto costosi e necessitano di numerose persone oltre di una certa quantità di tempo per essere realizzate. Al contrario i metodi indiretti (geofisici), oggetto del presente lavoro, consentono di indagare cavità di dimensioni anche ridotte in maniera estensiva oltre che generalmente rapida. Ciò premesso, nell’ambito del presente Dottorato, sono stati usati diversi metodi geofisici di near surface, integrandoli fra loro, allo scopo di caratterizzare le cavità presenti in due diversi test sites in ambito urbano ed extraurbano. La prima area test, indagata con il metodo GPR e il metodo ERT, è quella del Parco della Caffarella, in cui si ha una conoscenza solamente parziale di un esteso reticolo caveale scavato nelle pozzolane rosse dal quale si estraevano, in epoca etrusca e romana, materiali per l’edilizia. L’area indagata ha dimensioni 48 m x 30 m e la zona di sovrapposizione fra il metodo ERT e il GPR risulta essere di 48m x 14 m. Più in dettaglio, sono state eseguiti 14 profili ERT (modello Syscal Junior-Iris Instrument),aventi lunghezza 47 m con i 48 elettrodi posti ogni metro. L’array scelto è stato il doppio-dipolo poiché assicura una buona risoluzione sia in termini di variazioni verticali che orizzontali delle resistività, come ampiamente noto in letteratura. L’area in oggetto è stata indagata con il GPR (Modello SIR-3000, GSSI) usando dapprima un’antenna bistatica, ad offset costante, ad alta frequenza (400 MHz) e successivamente un’antenna monostatica a bassa frequenza (70 MHz). Nel primo caso i profili sono stati acquisiti con un’interdistanza pari a 0.5 m mentre nel secondo con un’interdistanza pari a 1 m. I dati sono stati elaborati con software specifici per estrarre delle sezioni tempo-profondità (time-slice) dell’area indagata con i dati GPR e delle sezione profondità bidimensionali (depth-slice) con i dati ERT. La seconda area è sita nel territorio di Magliano Sabina-Loc. Madonna del Giglio (Rieti), nella quale, da numerose fonti archeologiche è nota la presenza di strutture funerarie a fossa (VII-VI sec. a.C.), parzialmente collassate. L’area di dimensioni 80 m x 30 m è stata indagata, dapprima con il GPR (Modello SIR-3000,GSSI) usando un’antenna bistatica ad offset costante ad alta frequenza (400 MHz) acquisendo i profili ogni 0.5 m e successivamente con il magnetometro differenziale fluxgate (FM256-Geoscan Research), suddividendo l’area in 7 quadrati di 10 m di lato, con i profili paralleli acquisiti ogni metro e le misure lungo il profilo ogni 0.5 m. La zona di sovrapposizione fra i due metodi è stata di 70 m x 10 m. Anche in questo caso dai dati GPR sono state ricavate le time-slices mentre i dati magnetici sono stati elaborati con la crosscorrelazione normalizzata bidimesionale allo scopo di far emergere le anomalie da un contesto geologico altrimenti piuttosto rumoroso. Dopo le suddette operazioni, per entrambi i siti sono stati testati diversi metodi di integrazione sia di tipo qualitativo (Contour map overlay, RGB Colour Composite) che di tipo quantitativo (data sum, data product, binary representation) oltre di tipo statistico (Principal Component Analysis, K-mean Cluster analysis, Bayesian Maximum Entropy). I risultati, incoraggianti, mostrano come alcuni dei metodi summenzionati siano fin da ora spendibili in un contesto applicativo, mentre altri si trovino ad un livello di ricerca

Updating soil information with digital soil mapping

De Bodemkaart van Nederland, schaal 1:50.000, is de belangrijkste bron van bodeminformatie in Nederland. Deze kaart raakt echter in gebieden met veengronden verouderd. Door intensief gebruik van deze gronden verdwijnt het veen. Actualisatie van de bodemkaart is daarom noodzakelijk. Bas Kempen promoveerde op zijn onderzoek hiernaar